Systems and methods for predicting user activity based on historical activity information

ABSTRACT

Technologies for identifying prospective marketing targets based on payment vehicle-based payment transactions processed over electronic payment networks are disclosed. Payment vehicle-based payment transactions are analyzed to determine historical purchase activity progressions. Consumer behavior can be mapped to a historical purchase activity progression so that future spend behavior of the consumer can be identified.

BACKGROUND

Accurately predicting a consumer’s future spending behavior can allow afinancial institution (such as a credit company, lender, transactionprocessor, etc.) or another consumer services company (such as retailestablishment, food/beverage establishment, marketing firm, etc.) tobetter target potential prospects and identify opportunities to increaseconsumer transaction volumes.

BRIEF DESCRIPTION OF THE DRAWINGS

It is believed that certain embodiments will be better understood fromthe following description taken in conjunction with the accompanyingdrawings, in which like references indicate similar elements and inwhich:

FIG. 1 depicts an example expenditure tracking server that tracksconsumer spending behaviors across a plurality of merchants to determinehistorical purchase activity progressions;

FIG. 2 depicts an example expenditure tracking server defininghistorical purchase activity progressions;

FIGS. 3-4 depict consumer spending behavior of a consumer being assessedby an expenditure tracking server such that future spending behavior canbe predicted;

FIG. 5 depicts a block diagram of an example expenditure tracking systemwith an expenditure tracking server hosted by, or otherwise a componentof or affiliated with, an acquirer computing system; and

FIG. 6 depicts an example process in which an expenditure trackingserver provides an indication of at least one predicted purchasetransaction based on historical purchase activity.

DETAILED DESCRIPTION

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and use of the systems and methods disclosedherein. One or more examples of these non-limiting embodiments areillustrated in the selected examples disclosed and described in detailwith reference made to the figures in the accompanying drawings. Thoseof ordinary skill in the art will understand that systems and methodsspecifically described herein and illustrated in the accompanyingdrawings are non-limiting embodiments. The features illustrated ordescribed in connection with one non-limiting embodiment may be combinedwith the features of other non-limiting embodiments. Such modificationsand variations are intended to be included within the scope of thepresent disclosure.

For simplicity, the description that follows will be provided byreference to “payment vehicles” or “payment cards,” which generallyrefer to any type of financial alternative to currency (i.e.,cash/coins). As is to be clear to those skilled in the art, no aspect ofthe present disclosure is specifically limited to a specific type ofpayment vehicle or payment card. Therefore, it is intended that thefollowing description encompasses the use of the present disclosure withmany other forms of financial alternatives to currency, including creditcards, debit cards, smart cards, single-use cards, pre-paid cards,electronic currency (such as might be provided through a cellulartelephone or personal digital assistant), digital wallets, and the like.Payment vehicles or payment cards can be traditional plastic transactioncards, titanium-containing, or other metal-containing, transactioncards, clear and/or translucent transaction cards, foldable or otherwiseunconventionally-sized transaction cards, radio-frequency enabledtransaction cards, or other types of transaction cards, such as credit,charge, debit, pre-paid or stored-value cards, or any other likefinancial transaction instrument.

In accordance with the present disclosure, historical purchase activityprogressions can be determined based on historical purchase activity ofpayment card holders. Based on these historical purchase activityprogressions, future spending activities of consumers or segments ofconsumers can be predicted. Subsequently, various marketing efforts, orother business processes, can be employed to influence those futurespending activities. Beneficially, these prospective marketing effortscan be tailored so that they are directed to a targeted consumer base,such that conversion percentage, redemption volume, perceived relevance,or other desired outcome, can be increased.

In accordance with the present disclosure, purchase activity associatedwith payment vehicle-based purchased transactions originating at avariety of merchants can be collected for analysis. Through the analysisof transaction data associated with this purchase activity, certainbehavioral spending patterns can be identified. Historical purchaseactivity progressions can then be determined based on statisticalanalysis of the transaction data. For instance, the transaction data canindicate that consumers that make a first purchase at a particularmerchant and then make a second purchase at a second merchant within aparticular timeframe, are then likely to make a third purchase at aparticular merchant. The historical purchase activity progressions canidentify merchants by categories, such that a first purchase event at amerchant included within a first category, a second purchase event at amerchant included within a second category, and a third purchase eventat a merchant included with a third category is identified as a knownprogression, based on consumer spending patterns. The categories used tostructure historical purchase activity progressions can be defined bymerchant category code (i.e., MCC), merchant name, or othercategorizations, such as geography-based categorization, for example.Furthermore, historical purchase activity progressions can includetemporal parameters, such that each of the purchase transactionstypically occurs within a particular timeframe, such as within 1 hour, 1day, 1 week, for example. As is to be appreciated, any number ofhistorical purchase activity progressions can be determined. Moreover,the historical purchase activity progressions can be augmented, or evendeemed to be obsolete, over time as more transactional data is availablefor review and consumer purchasing behavior changes over time. Theanalysis of payment vehicle-based purchase transactions includes theassessment of non-human readable information and data, as thetransaction data collected can be received from point-of-sale devicesand systems of merchants that are configured to generate networkmessaging in accordance with various payment processing messagespecifications, such as ISO 8583, among others.

Once historical purchase activity progressions are determined throughthe review of the transaction data, they can then be used to predictfuture purchase transactions of consumers. A consumer’s spendingbehavior can be assessed to determine if it maps to, or otherwisefollows or matches, any of the historical purchase activityprogressions. When a consumer (or consumer segment) is determined to bemapped to a historical purchase activity progression, the futurespending activity of the consumer can be predicted based on the purchaseactivity identified in the particular progression or progressions towhich the consumer’s activity is mapped. For example, a particularhistorical purchase activity progression may identify a series of 7transactions that a consumer typically performs in a certain order overa certain period of time. It may be determined that a consumer hasperformed 5 of the transactions. In this case, the remaining 2transactions are predicted to occur. Using this prediction of futurespending activity, marketing efforts or other business processes can bedeployed to further influence or modify the predicted spending behavior.

Various approaches to mapping or modeling consumer behavior can beutilized in accordance with the present disclosure. For example,historical purchase activity progressions can be used in associationwith individual marketing campaigns of a single merchant, an ongoingmarketing program for a single merchant, or for a combination orcoalition of merchants or other entities. Behavior can be predictedbased on any of a variety of factors or parameters associated withtransactions, such as merchant identity, merchant category, time, day,and/or velocity that can be compared against parameters set forcampaigns, marketing programs, etc. If certain behavior of a consumer ispredicted, then offers, incentives, or other communications can beprovided to consumer through one or more channels. Furthermore,assessing and predicting behavior in accordance with the presentdisclosure can be used in a variety of implementations to provide arange of consumer-related data. For example, certain spend patterns orbehaviors of a consumer can indicate the consumer is likely to attrite,such as if a consumer misses a predetermined spend window. Based on thegeneration of the early attrition indicator, a merchant can usemarketing efforts to retain that consumer.

Referring now to FIG. 1 , an expenditure tracking server 112 is depictedthat tracks consumer spending across a plurality of merchants todetermine historical purchase activity progressions. Merchants A-H thataccept payment vehicles via point-of-sale systems or otherpoint-of-interaction devices are schematically depicted and aregenerally referred to herein as merchants 124. Furthermore, inaccordance with some embodiments, each merchant A through H depictedFIG. 1 can represent individual merchants, merchant category types,and/or other groupings of merchants (such as merchants grouped bygeography).

The merchants 124 can provide purchase transaction data to theexpenditure tracking server 112, as indicated by data streams 128. Insome cases, the data streams 128 are part of or wholly comprised ofpayment processing messaging, as is used to effectuate a payment vehicletransaction such that there is an electronic transfer of funds via anelectronic payment network. For example, the data streams 128 caninclude data supplied by POS systems of the merchants 124 to an acquirercomputing system. As such, the data streams 128 can include data usedfor authorization messaging, such as merchant identifier (MID), merchantcategory code (MCC), time/date information, and so forth. In someembodiments, the merchants 124 can have computing systems that providethe data streams 128 to the expenditure tracking server 112, such asthrough API calls or other data transfer techniques.

Various consumers are schematically shown initiating payment vehicletransactions at the merchants 124 using payment vehicles that aredepicted as PV1, PV2, and PV3, and generally referred to as paymentvehicles 122. While only three payment vehicles 122 are depicted in FIG.1 for the purposes of illustration, the payment vehicles 122 shown inFIG. 1 can be representative of hundreds, thousands, or hundreds ofthousands of payment vehicles, or segments or other groupings of paymentvehicles, as may be needed to statistically define historical purchaseactivity progressions. Referring first to PV1, a series of paymentvehicle-based payment transactions is shown to have occurred at merchantA, then merchant D, and finally at merchant G. A data stream 128 wasprovided by each merchant A, D, G to the expenditure tracking server112. As such, the expenditure tracking server 112 receives spendingbehavior of the cardholder of PV1. Similar data can be collected fromPV2 and PV3. As illustrated, the cardholder of PV2 initiated purchasetransactions at merchant E, then merchant C, and then merchant F. Thecardholder of PV3 initiated purchase transactions at merchant B, thenmerchant C, and then merchant H. Data streams 128 indicative of each ofthese transactions can be provided to the expenditure tracking server112.

The expenditure tracking server 112 can be embodied as any type ofcomputing device or server capable of processing, communicating,storing, maintaining, and transferring data. For example, theexpenditure tracking server 112 can be embodied as a microcomputer, aminicomputer, a mainframe, a desktop computer, a laptop computer, amobile computing device, a handheld computer, a smart phone, a tabletcomputer, a personal digital assistant, a telephony device, a customchip, an embedded processing device, or other computing device and/orsuitable programmable device. In some embodiments, the expendituretracking server 112 can be embodied as a computing device integratedwith other systems or subsystems, such as those of an acquirer computingsystem, a financial transaction processing gateway, and/or otherentities that function to assist with the processing of financialtransactions within a payment ecosystem. In the illustrative embodimentof FIG. 1 , the expenditure tracking server 112 includes a processor104, a system bus 106, a memory 108, a data storage 110, communicationcircuitry 116, and one or more peripheral devices 114. The expendituretracking server 112 can include other or additional components, such asthose commonly found in a server and/or computer (e.g., variousinput/output devices). Additionally, in some embodiments, one or more ofthe illustrative components can be incorporated in, or otherwise from aportion of, another component. For example, the memory 108, or portionsthereof, can be incorporated in the processor 104 in some embodiments.Furthermore, it should be appreciated that the expenditure trackingserver 112 can include other components, sub-components, and devicescommonly found in a computer and/or computing device, which are notillustrated in FIG. 1 for clarity of the description.

The processor 104 can be embodied as any type of processor capable ofperforming the functions described herein. For example, the processor104 can be embodied as a single or multi-core processor, a digitalsignal processor, microcontroller, a general purpose central processingunit (CPU), a reduced instruction set computer (RISC) processor, aprocessor having a pipeline, a complex instruction set computer (CISC)processor, an application specific integrated circuit (ASIC), aprogrammable logic device (PLD), a field programmable gate array (FPGA),or other processor or processing/controlling circuit or controller.

In various configurations, the expenditure tracking server 112 includesthe system bus 106 for interconnecting the various components of theexpenditure tracking server 112. The system bus 106 can be embodied as,or otherwise include, memory controller hubs, input/output control hubs,firmware devices, communication links (i.e., point-to-point links, buslinks, wires, cables, light guides, printed circuit board traces, etc.)and/or other components and subsystems to facilitate the input/outputoperations with the processor 104, the memory 108, and other componentsof the expenditure tracking server 112. In some embodiments, theexpenditure tracking server 112 can be integrated into one or more chipssuch as a programmable logic device or an application specificintegrated circuit (ASIC). In such embodiments, the system bus 106 canform a portion of a system-on-a-chip (SoC) and be incorporated, alongwith the processor 104, the memory 108, and other components of theexpenditure tracking server 112, on a single integrated circuit chip.

The memory 108 can be embodied as any type of volatile or non-volatilememory or data storage capable of performing the functions describedherein. For example, the memory 108 can be embodied as read only memory(ROM), random access memory (RAM), cache memory associated with theprocessor 104, or other memories such as dynamic RAM (DRAM), static RAM(SRAM), programmable ROM (PROM), electrically erasable PROM (EEPROM),flash memory, a removable memory card or disk, a solid state drive, andso forth. In operation, the memory 108 can store various data andsoftware used during operation of the expenditure tracking server 112such as operating systems, applications, programs, libraries, anddrivers.

The data storage 110 can be embodied as any type of device or devicesconfigured for short-term or long-term storage of data such as, forexample, memory devices and circuits, memory cards, hard disk drives,solid-state drives, or other data storage devices. For example, in someembodiments, the data storage 110 includes storage media such as astorage device that can be configured to have multiple modules, such asmagnetic disk drives, floppy drives, tape drives, hard drives, opticaldrives and media, magneto-optical drives and media, Compact Disc (CD)drives, Compact Disc Read Only Memory (CD-ROM), Compact Disc Recordable(CD-R), Compact Disc Rewriteable (CD-RW), a suitable type of DigitalVersatile Disc (DVD) or Blu-Ray disc, and so forth. Storage media suchas flash drives, solid state hard drives, redundant array of individualdisks (RAID), virtual drives, networked drives and other memory meansincluding storage media on the processor 104, or the memory 108 are alsocontemplated as storage devices. It should be appreciated that suchmemory can be internal or external with respect to operation of thedisclosed embodiments. It should also be appreciated that certainportions of the processes described herein can be performed usinginstructions stored on a computer-readable medium or media that director otherwise instruct a computer system to perform the process steps.Non-transitory computer-readable media, as used herein, comprises allcomputer-readable media except for transitory, propagating signals.

The communication circuitry 116 of the expenditure tracking server 112can be embodied as any type of communication circuit, device, interface,or collection thereof, capable of enabling communications between theexpenditure tracking server 112 and computing devices communicativelycoupled thereto. For example, the communication circuitry 116 can beembodied as one or more network interface controllers (NICs), in someembodiments. The communication circuitry 116 can be configured to useany one or more communication technologies (e.g., wireless or wiredcommunications) and associated protocols (e.g., Ethernet, Wi-Fi®, WiMAX,etc.) to effect such communication. The expenditure tracking server 112can communicate over one or more networks. The network(s) can beembodied as any number of various wired and/or wireless communicationnetworks. For example, the network(s) can be embodied as or otherwiseinclude a local area network (LAN), a wide area network (WAN), acellular network, or a publicly-accessible, global network such as theInternet. Additionally, the network(s) can include any number ofadditional devices to facilitate communication with other computingdevices.

Additionally, in some embodiments, the expenditure tracking server 112can further include one or more peripheral devices 114. Such peripheraldevices 114 can include any type of peripheral device commonly found ina computing device such as additional data storage, speakers, a hardwarekeyboard, a keypad, a gesture or graphical input device, a motion inputdevice, a touchscreen interface, one or more displays, an audio unit, avoice recognition unit, a vibratory device, a computer mouse, aperipheral communication device, and any other suitable user interface,input/output device, and/or other peripheral device.

Referring now to FIG. 2 , the expenditure tracking server 112 can trackthe transactions initiated at the merchants 124 that utilize the paymentvehicles 122. Based on these tracked transactions, historical purchaseactivity progressions, shown as HPAP1, HPAP2, and HPAP3 in FIG. 2 andgenerally referred to as historical purchase activity progressions 130,can be determined and stored in data storage 110. As is to beappreciated, the historical purchase activity progressions 130 can bebased on the processing and analysis of large amounts of transactionaldata over periods of time. Once determined by the expenditure trackingserver 112, each historical purchase activity progression 130 can beused to identify various predicted consumer purchase transactions basedon the determination that the consumer’s purchase activities are mappedto a particular historical purchase activity progression. The predictedconsumer purchase transactions are generally purchase transactions theconsumer is likely to make in the future based on their past purchaseactivity. While the particular purchase activity that is identified canvary, examples include an identification of a particular merchant, anidentification of a geographical location of future purchase activity,an identification of a particular merchant category, an identificationof a product or service likely to be purchased, so forth. For thesimplified example depicted in FIGS. 1-2 , each historical purchaseactivity progression 130 identifies a string of merchants and a temporalfactor (shown as T1 and T2) identifying the amount of time, or a timerange, between the transactions that occur at the merchants. In someembodiments, historical purchase activity progressions are embodied asmarketing campaigns defined by a merchant or collection of merchants.For instance, the marketing campaign can identify that when a consumerinitiates certain types of transactions, then the consumer will beprovided with an outcome (i.e., coupon, offer, or other type ofincentive or reward) through a suitable communication channel.

While the historical purchase activity progressions 130 areschematically shown to include three merchants, it is to be appreciatedthat historical purchase activity progressions 130 can include anynumber of merchants, merchant categories, geographical parameters,temporal parameters, as well as any other data that may be useful inmapping consumer behaviors.

Referring now to FIGS. 3-4 , the consumer spending behavior of aconsumer 120 can be assessed by the expenditure tracking server 112 suchthat future spending behavior can be predicted. Referring first to FIG.3 , the consumer 120 first uses a payment vehicle, schematicallydepicted as consumer payment vehicle 132, to initiate a purchase atmerchant E. As described above with regard to FIG. 1 , merchant E canprovide a data stream 128 (such as an authorization request, forexample) to the expenditure tracking server 112. After a period of time,the consumer 120 then uses their consumer payment vehicle 132 toinitiate a purchase at merchant C. Merchant C can provide a data stream128 to the expenditure tracking server 112. It is noted that consumerpayment vehicle 132 represents either a single payment vehicle, or acollection of payment vehicles tied to the consumer 120. While FIG. 3depicts the consumer using the same payment vehicle 132 as merchant Eand merchant C, this disclosure is not so limited. In some embodiments,the expenditure tracking server 112 can track the consumer’s activityacross more than one payment vehicle. Furthermore, in some embodiments,the expenditure tracking server 112 can track collectively tracktransaction activities for more than one consumer that are within thesame household.

Referring now to FIG. 4 , the expenditure tracking server 112 isdepicted generating an indication 134 of at least one predicted purchasetransaction based on payment vehicle-based payment transaction datareceived via the data streams 128. More specifically, based on thereceived transaction data from the consumer’s 120 purchase activity 136the expenditure tracking server 112 can attempt to map the purchaseactivity 136 to one or more historical purchase activity progressions130. As indicated in FIG. 4 , the expenditure tracking server 112identifies that the purchase activity 136 of the consumer 120 maps toHPAP2. While FIG. 4 depicts a mapping based on merchant and a timeparameter, in some other embodiments, mapping is based on, for example,geographical parameters, merchant categories, purchase velocity,purchase amounts, SKU data, and so forth.

Based on the mapping to HPAP2, expenditure tracking server 112identifies that purchase activity is predicted to occur at merchant Fafter the time period identified as T2. This predicted purchase activitycan be provided to interested parties, such as marketing agencies,merchant, card issuers, financial institutions, and the like. Based onreceiving the predicted purchase activity, the recipient can take avariety of actions to influence the behavior of the consumer 120. Forexample, the recipient can transmit a targeted offer in an attempt toinfluence the consumer’s 120 predicted purchasing event(s).

FIG. 5 depicts a block diagram of an example expenditure tracking system200. In the illustrated embodiment, an expenditure tracking server 212is hosted by, or otherwise a component of or affiliated with an acquirercomputing system 250. The acquirer computing system 250 is configured tocommunicate point of sale (POS) systems 226 and communicate with one ormore payment networks 260 and issuer computing systems 262. Forconvenience, only one POS system 226 and one issuer computing system 262is shown. Moreover, as used herein, the term POS system is used broadlyto include POS system or point of interaction system at brick and mortarlocations and “virtual” POS system that can be associated with an onlineretailor or “in-app” purchases. In some cases, the POS system includes aterminal, or other network computing system which can be used tofacilitate a payment transaction at a merchant location. The POS system226 is affiliated with a merchant 224. The term merchant, as usedherein, refers generally to any type of retailer, service provider, orany other type of business that is in networked communication with theacquirer computing system 250 and uses the payment processing servicesof the acquirer computing system 250. Payment processing services caninclude receiving and responding to authorization requests as well asfacilitating the settlement of funds associated with card-basedtransactions occurring at the merchant 224.

In some embodiments, POS system 226 can generally facilitate thetransmission of transaction-related information to the acquirercomputing system 226, as is known in the art. The transaction-relatedinformation can comprise an authorization request as well as other typesof identifying indicia. The identifying indicia can vary based on POSsystem 226, the type of merchant and the type of transaction, butexample types of identifying indicia can include any of the following: amerchant identification (MID) identifier, a loyalty program identifier,a bank identification (BIN) identifier; a merchant category code (MCC)identifier; a media access control (MAC) identifier; an internetprotocol (IP) identifier; a device fingerprint; a geographic identifier;a payment type identifier; and/or a consumer name or other consumeridentifier. In some embodiments, the information provided to theacquirer computing system 226 and/or the expenditure tracking server 212can include SKU data 234.

In accordance with the present disclosure, the expenditure trackingserver 212 of acquirer computing system 250 can provide an HPAPinterface 280 that is accessible by a receiving entity 270 through acomputing device 268. The particular implementation of the HPAPinterface 280 can vary, but in some example embodiments, the expenditureinterface 280 is a web portal that allows a receiving entity 270 toreview historical purchase activity progressions, review indications offuture purchasing activity, and so forth. In some embodiments, the HPAPinterface 280 is provided by a specialized application that is executedon the computing device 268. The receiving entity 270 can be associatedwith, for example, the merchant 224, the issuer financial institution264, or any other third party, such as a marketing entity. The dataregarding the transactions tracked by the expenditure tracking server212 can be stored in a historical purchase activity database 216.Historical purchase activity progressions can be stored in a HPAPdatabase 214.

Referring still to the acquirer computing system 250, an authorizationrequest can be received from the POS system 226 through communicationchannel 228 based on the use of a payment vehicle 222. The firstauthorization request can comprise various data, including, for example,a MID, a MCC, an account identifier, and a transaction amount. Once theauthorization request is received, the predicted purchase transactioncomputation module 218 can determine if the purchase activity of theconsumer 220 maps to a historical purchase activity progression of theHPAP database 214.

When it is determined that future spending behavior of the consumer 220can be predicted, the indication of the future purchase activity can bepresented to any suitable parties, depicted as receiving entity 270. Inone embodiment, the indication is utilized to develop or otherwiseidentify a targeted offer 278 to provide to the consumer 220. Thus, suchtargeted offer 278 can be aimed to incentivize certain future behaviorsbased on the historical expenditure of the consumer 220. The targetedoffer 278 can be stored by the expenditure tracking server 212, suchthat upon mapping the spending behavior of the consumer 220 to ahistorical purchase activity progression, a targeted offer associatedwith predicted purchase activity of that historical purchase activityprogression can be provided to the consumer 220. In some embodiments,the targeted offer 224 can be dispatched to prospective marketingtargets in an automated fashion based on the outcome of the HPAPanalysis.

Referring now to FIG. 6 , an example process 300 is depicted in which anexpenditure tracking server, such as expenditure tracking server 212shown in FIG. 5 , provides an indication of at least one predictedpurchase transaction based on historical purchase activity. At block302, purchase vehicle-based payment transactions are processed on behalfof a plurality of merchants, such as the merchant 224 shown FIG. 5 .Such processing can be performed by an acquirer computing system, suchas acquirer computing system 250 shown in FIG. 5 . For each of thepayment vehicle-based payment transactions that is processed,transaction data can be received from a point of sale system of themerchants transmitting the transactions. At 304, transaction data foreach of a plurality of payment transactions is stored so that dataanalytics can be performed. Referring again to FIG. 5 , for example, thetransaction data can be stored in historical purchase activity database216. At 306 of FIG. 6 , an expenditure tracking server determines aplurality of historical purchase activity progressions. Each of thehistorical purchase activity progressions can identify a series ofpayment vehicle-based payment transaction types based on an analysis ofthe transaction data. The payment vehicle-based payment transactiontypes can include, for example, a merchant type, a merchant category, ageographic parameter, and so forth. The historical purchase activityprogression can be stored in the HPAP database 214, as shown in FIG. 5 .At 308, the acquirer computing system processes a first paymentvehicle-based payment transaction for a consumer at a first merchant. At310, the acquirer computing system processes a second paymentvehicle-based payment transaction for the consumer at a second merchant.At 312, the expenditure tracking server determines whether theprogression of the consumer from the first merchant to the secondmerchant maps to one of the plurality of historical purchase activityprogressions. For instance, the expenditure tracking server candetermine if there is a historical purchase activity progression thatincludes a purchase at the first merchant that is followed by a purchaseat the second merchant, with the second purchase occurring within aparticular timeframe. If the progression of the consumer from the firstmerchant to the second merchant does not map to one of the plurality ofhistorical purchase activity progressions, the transaction data from thefirst and second payment vehicle-based payment transactions can bestored at 316. If the progression of the consumer from the firstmerchant to the second merchant does map to one of the plurality ofhistorical purchase activity progressions, at 314, an indication of atleast one predicted purchase transaction based on the historicalpurchase activity progression to which the progression of the consumermapped can be provided to a recipient so that action can be taken basedon the predicted purchase transaction.

By way of a non-limiting example, the expenditure tracking server canidentify a historical purchase activity progression that identifiesconsumers who make a purchase at a first gas station and then make apurchase at a second gas station that is more than 200 miles away arelikely to make a third purchase at a hotel. Accordingly, when a consumerfills up their vehicle at a first gas station and then makes a purchaseat a second gas station that is more than 200 miles away, theexpenditure tracking server can then determine that the consumer islikely to be looking for a hotel. A notification of this predictedpurchase activity can be provided to a suitable recipient, such as atravel company, hotel marketing department, the consumer, or otherentity so that action can be taken to modify that consumer’s spendingbehavior. For example, a hotel chain may offer the consumer a discountcoupon or other offer to motivate the consumer to book a room at theirhotel chain. The notification can be provided using any of a variety ofsuitable techniques. For notifications provided to a consumer, such asin the form of a targeted offer, the notification can be presented tothe consumer via a computing device of the consumer (i.e., emailmessage, text message, social media message, in-app message, etc.) orotherwise tied to a payment vehicle of the consumer (i.e., card-linkedoffer, etc.). For notifications provided to third parties, such asreporting provided to marketing companies, merchants, and/or financialinstitutions, the notification can be provided via an API interface, adata file, an email message, or via a web-based portal or interface thatis accessible by the third party. In another non-limiting example, amove theater can utilize an expenditure tracking server to identifyrestaurant transactions satisfying certain parameters that occur withina certain geo-radius of theater. If the parameters are satisfied, anoffer could be generated and delivered to the consumer through anysuitable channel to incentive the consumer to see a movie at thetheater. Such parameters could include, for example, the type ofrestaurant, the amount of the transaction, the time of the transaction,the date of the transaction, and so forth.

The systems, apparatuses, devices, and methods disclosed herein aredescribed in detail by way of examples and with reference to thefigures. The examples discussed herein are examples only and areprovided to assist in the explanation of the apparatuses, devices,systems and methods described herein. None of the features or componentsshown in the drawings or discussed herein should be taken as mandatoryfor any specific implementation of any of these the apparatuses,devices, systems or methods unless specifically designated as mandatory.In addition, elements illustrated in the figures are not necessarilydrawn to scale for simplicity and clarity of illustration. For ease ofreading and clarity, certain components, modules, or methods may bedescribed solely in connection with a specific figure. In thisdisclosure, any identification of specific techniques, arrangements,etc. are either related to a specific example presented or are merely ageneral description of such a technique, arrangement, etc.Identifications of specific details or examples are not intended to be,and should not be, construed as mandatory or limiting unlessspecifically designated as such. Any failure to specifically describe acombination or sub-combination of components should not be understood asan indication that any combination or sub-combination is not possible.It will be appreciated that modifications to disclosed and describedexamples, arrangements, configurations, components, elements,apparatuses, devices, systems, methods, etc. can be made and may bedesired for a specific application. Also, for any methods described,regardless of whether the method is described in conjunction with a flowdiagram, it should be understood that unless otherwise specified orrequired by context, any explicit or implicit ordering of stepsperformed in the execution of a method does not imply that those stepsmust be performed in the order presented but instead may be performed ina different order or in parallel.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment,” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment, or "in anembodiment,” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures or characteristics may be combined in any suitablemanner in one or more embodiments.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware. The term “software”is used expansively to include not only executable code, for examplemachine-executable or machine-interpretable instructions, but also datastructures, data stores and computing instructions stored in anysuitable electronic format, including firmware, and embedded software.The terms “information” and “data” are used expansively and include awide variety of electronic information, including executable code;content such as text, video data, and audio data, among others; andvarious codes or flags. The terms “information,” “data,” and “content”are sometimes used interchangeably when permitted by context. It shouldbe noted that although for clarity and to aid in understanding someexamples discussed herein might describe specific features or functionsas part of a specific component or module, or as occurring at a specificlayer of a computing device (for example, a hardware layer, operatingsystem layer, or application layer), those features or functions may beimplemented as part of a different component or module or operated at adifferent layer of a communication protocol stack. Those of ordinaryskill in the art will recognize that the systems, apparatuses, devices,and methods described herein can be applied to, or easily modified foruse with, other types of equipment, can use other arrangements ofcomputing systems such as client-server distributed systems, and can useother protocols, or operate at other layers in communication protocolstacks, than are described.

The foregoing description of embodiments and examples has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or limiting to the forms described. Numerous modificationsare possible in light of the above teachings. Some of thosemodifications have been discussed, and others will be understood bythose skilled in the art. The embodiments were chosen and described inorder to best illustrate principles of various embodiments as are suitedto particular uses contemplated. The scope is, of course, not limited tothe examples set forth herein, but can be employed in any number ofapplications and equivalent devices by those of ordinary skill in theart. Rather it is hereby intended the scope of the invention to bedefined by the claims appended hereto.

1-20. (canceled)
 21. A computer-implemented method for predicting useractivities, comprising: receiving, by a server, activity informationassociated with a plurality of users from a plurality of terminals;storing, in a database associated with the server, the activityinformation associated with the plurality of users; processing, by theserver and using a processor, the activity information to determinebehavioral patterns and/or historical activities progression associatedwith at least one user, wherein the historical activities progressionincludes a series of activities performed by the at least one userwithin a predetermined time period; mapping, by the server and using acommunication circuitry, the behavioral patterns to the historicalactivities progression to predict one or more activities of the at leastone user; and automatically generating, by the server and using adisplay unit, an offer associated with the one or more predictedactivities within the predetermined time period in a user interface of adevice associated with the at least one user.
 22. Thecomputer-implemented method of claim 21, wherein the historicalactivities progression further includes a historical geographicalparameter between each successive pair of the series of activitiesperformed by the at least one user within the predetermined time period.23. The computer-implemented method of claim 21, further comprising:determining, by the server, a current activity progression for the atleast one user, wherein the current activity progression identifies oneor more completed activities from the series of activities performed bythe at least one user, a current temporal factor between the one or morecompleted activities, and a current geographical parameter between theone or more completed activities.
 24. The computer-implemented method ofclaim 23, wherein mapping the behavioral patterns to the historicalactivities progression, further comprises: matching, by the processor,the one or more completed activities by the at least one user to asuccessive pair of the historical activities progression stored in thedatabase.
 25. The computer-implemented method of claim 23, whereinmapping the behavioral patterns to the historical activitiesprogression, further comprises: matching, by the processor, the currentgeographical parameter to a historical geographical parametercorresponding to successive pair of the historical activitiesprogression stored in the database.
 26. The computer-implemented methodof claim 23, further comprising: predicting, by the server, anoccurrence of at least one of remaining series of activities pursuant toat least one in-progress activity upon satisfaction of at least oneparameter, wherein the at least one parameter include a location withina geo-radius of the in-progress activity.
 27. The computer-implementedmethod of claim 21, further comprising: generating, by the server, anotification pertaining to the predicted activities in the userinterface of the device associated with at least one service provider,wherein the at least one service provider performs one or more actionsto influence the behavioral patterns of the at least one user pertainingto the predicted activities.
 28. The computer-implemented method ofclaim 21, further comprising: generating, by the server, a notificationpertaining to the historical activities progression in the userinterface of the device associated with at least one service provider,wherein the historical activities progression interface is a web portalconfigured to allow the at least one service provider to review thehistorical activities progression.
 29. The computer-implemented methodof claim 21, wherein each activity of the historical activityprogression identifies at least one service provider.
 30. Thecomputer-implemented method of claim 21, further comprising:determining, by the server, a progression to another activity by the atleast one user does not map to the historical activities progression;and storing, by the server, the activity information to another activityin the database.
 31. A system for predicting user activities,comprising: one or more processors; and at least one non-transitorycomputer readable medium storing instructions which, when executed bythe one or more processors, cause the one or more processors to performoperations comprising: receiving, by a server, activity informationassociated with a plurality of users from a plurality of terminals;storing, in a database associated with the server, the activityinformation associated with the plurality of users; processing, by theserver and using a processor, the activity information to determinebehavioral patterns and/or historical activities progression associatedwith at least one user, wherein the historical activities progressionincludes a series of activities performed by the at least one userwithin a predetermined time period; mapping, by the server and using acommunication circuitry, the behavioral patterns to the historicalactivities progression to predict one or more activities of the at leastone user; and automatically generating, by the server and using adisplay unit, an offer associated with the one or more predictedactivities within the predetermined time period in a user interface of adevice associated with the at least one user.
 32. The system of claim31, wherein the historical activities progression further includes ahistorical geographical parameter between each successive pair of theseries of activities performed by the at least one user within thepredetermined time period.
 33. The system of claim 31, furthercomprising: determining, by the server, a current activity progressionfor the at least one user, wherein the current activity progressionidentifies one or more completed activities from the series ofactivities performed by the at least one user, a current temporal factorbetween the one or more completed activities, and a current geographicalparameter between the one or more completed activities.
 34. The systemof claim 33, wherein mapping the behavioral patterns to the historicalactivities progression, further comprises: matching, by the processor,the one or more completed activities by the at least one user to asuccessive pair of the historical activities progression stored in thedatabase.
 35. The system of claim 33, wherein mapping the behavioralpatterns to the historical activities progression, further comprises:matching, by the processor, the current geographical parameter to ahistorical geographical parameter corresponding to successive pair ofthe historical activities progression stored in the database.
 36. Thesystem of claim 33, further comprising: predicting, by the server, anoccurrence of at least one of remaining series of activities pursuant toat least one in-progress activity upon satisfaction of at least oneparameter, wherein the at least one parameter include a location withina geo-radius of the in-progress activity.
 37. The system of claim 31,further comprising: generating, by the server, a notification pertainingto the predicted activities in the user interface of the deviceassociated with at least one service provider, wherein the at least oneservice provider performs one or more actions to influence thebehavioral patterns of the at least one user pertaining to the predictedactivities.
 38. A non-transitory computer readable medium for predictinguser activities, the non-transitory computer readable medium storinginstructions which, when executed by one or more processors, cause theone or more processors to perform operations comprising: receiving, by aserver, activity information associated with a plurality of users from aplurality of terminals; storing, in a database associated with theserver, the activity information associated with the plurality of users;processing, by the server and using a processor, the activityinformation to determine behavioral patterns and/or historicalactivities progression associated with at least one user, wherein thehistorical activities progression includes a series of activitiesperformed by the at least one user within a predetermined time period;mapping, by the server and using a communication circuitry, thebehavioral patterns to the historical activities progression to predictone or more activities of the at least one user; and automaticallygenerating, by the server and using a display unit, an offer associatedwith the one or more predicted activities within the predetermined timeperiod in a user interface of a device associated with the at least oneuser.
 39. The non-transitory computer readable medium of claim 38,further comprising: determining, by the server, a current activityprogression for the at least one user, wherein the current activityprogression identifies one or more completed activities from the seriesof activities performed by the at least one user, a current temporalfactor between the one or more completed activities, and a currentgeographical parameter between the one or more completed activities. 40.The non-transitory computer readable medium of claim 39, wherein mappingthe behavioral patterns to the historical activities progression,further comprises: matching, by the processor, the one or more completedactivities by the at least one user to a successive pair of thehistorical activities progression stored in the database and/or thecurrent geographical parameter to a historical geographical parametercorresponding to successive pair of the historical activitiesprogression stored in the database.